Method for evaluating minimum release energy of distributed energy storage emergency control based on improved fox algorithm

Abstract

This invention discloses a method for evaluating the minimum energy release in emergency control of distributed energy storage based on an improved FOX algorithm. This method builds a frequency response model of the power system under emergency control involving distributed energy storage, and solves for the optimal total release of distributed energy storage by constructing a model based on an improved FOX algorithm using grid pattern search. The energy optimization model aims to minimize the released energy, taking into account system stability and timeliness. It is not only reliable to implement but also provides good reference results.

Description

Technical Field

[0001] This invention relates to the field of distributed energy storage control technology, and in particular to a method for assessing the minimum energy release for emergency control of distributed energy storage based on an improved FOX algorithm. Background Technology

[0002] With the establishment of the carbon neutrality goal, new energy power generation technologies are developing rapidly. Distributed energy has been widely used due to its advantages of being pollution-free and low-cost. However, in high-proportion new energy power systems, a single short-circuit fault can easily cause a large-scale inrush of new energy into low-voltage ride-through, subjecting the system to large-capacity short-term power surges. This may lead to the system's maximum transient frequency deviation exceeding the low-frequency load shedding threshold, resulting in load losses. To address this issue, researchers are considering introducing distributed energy storage into the power system to cope with transient frequency exceedances under short-term power surges.

[0003] Current literature on emergency control energy assessment for energy storage mainly focuses on solutions for selecting emergency control strategies for power systems based on centralized energy storage, without addressing the application of distributed energy storage. Furthermore, the strategy selection methods are computationally expensive and suffer from limitations in versatility. Therefore, this patent provides a method for assessing the minimum energy release for emergency control of distributed energy storage based on an improved FOX algorithm. This method aims to help the power system recover quickly while avoiding frequency overshoot caused by excessive energy release. Summary of the Invention

[0004] In view of this, the purpose of this invention is to propose a reliable and reliable method for assessing the minimum energy release for emergency control of distributed energy storage based on the improved FOX algorithm.

[0005] To achieve the above-mentioned technical objectives, the technical solution adopted by this invention is as follows:

[0006] A method for assessing the minimum energy release for emergency control of distributed energy storage based on an improved FOX algorithm, comprising:

[0007] A frequency response model is constructed and segmented. The frequency response process of the distributed energy storage system is analyzed according to different time periods to determine and calculate the frequency minimum value and its magnitude in each time period. Based on this, an energy release optimization model for emergency control of distributed energy storage is constructed.

[0008] By combining grid pattern search with the FOX algorithm, an improved FOX algorithm model is constructed and used for parameter optimization of the energy release optimization model. Then, the minimum release energy of the distributed energy storage system under short-circuit fault is evaluated through the energy release optimization model.

[0009] As one possible implementation, this solution also includes:

[0010] The minimum energy release for emergency control of a distributed energy storage system under short-circuit fault is assessed, and then the output task is allocated based on the capacity of each distributed energy storage unit within the system.

[0011] As a preferred implementation option, this scheme constructs a frequency response model, segments it, and analyzes the frequency response process of the distributed energy storage system according to different time periods to determine and calculate the frequency minimum values ​​and their magnitudes for each time period, including:

[0012] 1.1 Constructing a frequency response model for distributed energy storage to participate in emergency control

[0013] The total energy released by each distributed energy storage unit in the distributed energy storage system, P s As a disturbance, the mathematical model of the frequency response of a distributed energy storage system is defined as follows:

[0014]

[0015] Wherein: Δω represents the system frequency response function; ΔG represents the frequency-active power transfer function of the distributed energy storage system; ΔP is the power disturbance; M and L refer to the rotor time constant and active frequency response coefficient related to the moment of inertia, respectively;

[0016] The distributed energy storage system is used for energy storage in new energy power generation. By ignoring the influence of load during transient processes, the non-step power disturbance is composed only of the short-term impact power during the low-voltage period of the new energy source and the energy released by the energy storage. Its frequency domain expression is defined as follows:

[0017]

[0018] Wherein: P is the short-term impact power generated during the low-voltage period of the new energy source; P s t1 represents the total energy released by distributed energy storage; t2 represents the low-voltage recovery time of the new energy source; t3 represents the energy release duration of the energy storage; t y To control the start-up delay time in emergencies;

[0019] Substituting equation (2) into equation (1), the time-domain function of the frequency response is obtained through the inverse Laplace transform:

[0020]

[0021] Wherein, ε represents the unit step function;

[0022] 1.2 Frequency Response Stage Analysis

[0023] Taking 50Hz as the frequency under normal operating conditions, based on the model formula of the frequency response model, the formula for the frequency of the distributed energy storage system under short-term disturbances is defined as follows:

[0024]

[0025] Here, the time interval containing t corresponds to different segments of the model.

[0026] As a preferred implementation option, the frequency response stage analysis of this scheme preferably also includes:

[0027] 1) 0 ≤ t < t1 + t y stage

[0028] During this phase, the frequency of the distributed energy storage system continues to drop, but the rate of drop gradually slows down. To ensure that the energy storage system releases energy before the system frequency reaches its lowest point, an emergency control start-up delay time t is implemented. y The following constraints must be met:

[0029]

[0030] 2)t1+t y ≤t<t1+t y +t3 stage

[0031] During this stage, energy storage begins to release energy, helping the distributed energy storage system return to normal operation. At this point, the impact of energy storage on the distributed energy storage system can be divided into two scenarios:

[0032] like In this scenario, distributed energy storage can immediately restore the frequency from a decrease to an increase. There exists a minimum point, and the formulas for the time and frequency of this point are as follows:

[0033]

[0034] In the formula: t b1 The time for the first minimum point; f b1 This represents the frequency of the first local minimum point;

[0035] like In this case, the total output of distributed energy storage is relatively small and cannot immediately change the rate of change of the system frequency. Therefore, it is discussed in two categories:

[0036] The first type is where the energy storage output is too small, and the frequency continues to drop even after emergency control measures are implemented. s And t3 satisfy the following relationship:

[0037]

[0038] The second type is energy storage, which can enable the system to recover and rise during emergency control periods, Ps The relationship between t3 and t3 is the opposite of that in the first type. This type of case also has a local point, and the formulas for the time and frequency of this point are as follows:

[0039]

[0040] 3)t1+t y +t3≤t<t1+t2 stage

[0041] During this phase, distributed energy storage exits emergency control, but the power deficit causes the system frequency to drop again. This can be discussed in two scenarios:

[0042] The first scenario involves excessive energy release from distributed energy storage, causing the system to overshoot in the second stage. In this stage, the overshoot decreases and approaches normal operating conditions, requiring the following relationship to be satisfied:

[0043]

[0044] There is no second minimum point in this case;

[0045] The second scenario is that the energy released by the distributed energy storage is insufficient to cause overshoot in the distributed energy storage system, but the frequency recovers slowly under the influence of system inertia and damping. In this case, there is a second minimum point, which must satisfy the opposite relationship to the first scenario. The time and frequency of the minimum point are determined by the following formulas:

[0046]

[0047] In the formula: t b2 The time for the second minimum point; f b2 This represents the frequency of the second minimum point.

[0048] As a preferred implementation option, the energy release optimization model for distributed energy storage emergency control in this scheme preferably includes:

[0049] 1.3 Constructing an energy release optimization model

[0050] The optimization model is defined as follows, with the minimum energy released from energy storage M as the objective function for the control strategy:

[0051]

[0052] Wherein: f limit This is the lowest system frequency; f b The frequency of each local minimum point; P represents the rate of change of the system's frequency before the distributed energy storage system ceases to output power; sN It is the sum of the rated power of distributed energy storage.

[0053] As a preferred implementation option, this scheme combines grid pattern search with the FOX algorithm to construct an improved FOX algorithm, including:

[0054] 1.4 Constructing an improved FOX algorithm model

[0055] The improved FOX algorithm consists of three stages. The first stage is the development stage, which is divided into two development strategies. The calculation formula is shown below:

[0056]

[0057] In the formula: Jump it Dis_FP is the jump height. it Dis_ST is the prey distance. it Sp_S represents the distance sound travels; Time_ST represents the speed of sound propagation; Sp_S represents the distance sound travels. it A random number between 0 and 1; BestX it The optimal population position is represented by `it`, which is the current iteration number.

[0058] Based on the random variable P, two strategies will be used for position updates, as shown in the following formulas:

[0059]

[0060] In the formula: X (it+1) The updated individual variables; c1 and c2 are 0.18 and 0.82 respectively; p is a random number between 0 and 1;

[0061] The second stage is the exploration stage, and the formula is as follows:

[0062]

[0063] In the formula: d represents the population dimension; MinT is the minimum time variable; a is the control variable; max it tt represents the maximum number of iterations; tt represents the propagation time.

[0064] The third stage employs a grid search, establishing a search grid centered on the current optimal location with a set step size. The grid establishment method is as follows:

[0065] x i =x0+v(j)·L s (13) Where: x0 and x i These refer to the starting point and grid point, respectively; v(j) refers to the mode vector; L s Refers to the search step size;

[0066] After establishing the search grid, select the points with better fitness from the grid points as the starting point for the next iteration. After multiple iterations, explore a better solution.

[0067] As a preferred implementation option, this scheme preferably uses the improved FOX algorithm model for parameter optimization of the energy release optimization model in equation (9), with P s A random population is constructed using t3 as the individual variable, releasing energy P. s *t3 is the fitness calculation formula. It iteratively updates the variables of individuals in the population and selects the individual with the smallest fitness as the optimal solution.

[0068] As a preferred implementation option, the improved FOX algorithm model described in this scheme is preferably based on the IEEE 10-machine 39-node system.

[0069] By adopting the above technical solution, the present invention has the following advantages compared with the prior art: The present solution method builds a frequency response model of the power system under emergency control by distributed energy storage, and solves the optimal total release of distributed energy storage by constructing a model based on the improved FOX algorithm of grid pattern search. The energy optimization model takes the minimum release of energy as the objective, and considers the system stability and timeliness. It is not only reliable to implement, but also has good reference value for the results. Attached Figure Description

[0070] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 This is a simplified implementation flowchart of the proposed method.

[0072] Figure 2 This is a simplified structural diagram of the IEEE 10-machine 39-node simulation system on which the proposed method is based;

[0073] Figure 3 This is a flowchart of the improved FOX algorithm in this scheme.

[0074] Figure 4 This is a comparison graph of the frequency response curves of the proposed method. Detailed Implementation

[0075] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] This implementation scheme provides a method for assessing the minimum energy release for emergency control of distributed energy storage based on an improved FOX algorithm, which roughly includes the following:

[0077] First, considering the impact of distributed energy storage participating in the emergency control of the power system, a frequency response model is constructed;

[0078] Secondly, the model is segmented and the response process of the system frequency is analyzed according to different time periods. The frequency minimum value and its value are determined and calculated for each time period. Based on the above evaluation method, an energy release optimization model for emergency control of distributed energy storage is constructed.

[0079] Furthermore, the grid pattern search method is combined with the FOX algorithm to construct an improved FOX algorithm model. Finally, based on the IEEE 10-machine 39-node example, the proposed improved FOX algorithm is used to calculate the minimum total energy release M for emergency control of distributed energy storage under short-circuit faults, and the output tasks are allocated according to the capacity of each distributed energy storage unit in the system.

[0080] Specific implementation schemes are as follows: Figure 1 As shown, this solution specifically includes the following steps:

[0081] 1.1 Frequency Response Model for Distributed Energy Storage in Emergency Control

[0082] This patent will calculate the total energy released by each distributed energy storage system within the system, P. s As a disturbance quantity, to facilitate the analysis of the impact of distributed energy storage on system frequency recovery, the calculation process of the system's frequency response is as follows:

[0083]

[0084] In the formula: Δω represents the system frequency response function; ΔG represents the system frequency-active power transfer function; ΔP is the power disturbance; M and L refer to the rotor time constant and active frequency response coefficient related to the moment of inertia, respectively.

[0085] To simplify the analysis, the influence of load during the transient process is ignored, and the non-step power disturbance is composed only of the short-term impact power during the low-voltage period of the new energy source and the energy released by the energy storage. Its frequency domain expression is as follows:

[0086]

[0087] In the formula: P is the short-term impact power generated during the low-voltage period of the new energy source; P s t1 represents the total energy released by distributed energy storage; t2 represents the low-voltage recovery time of the new energy source; t3 represents the energy release duration of the energy storage; t y Delay time for emergency control activation.

[0088] Substituting equation (2) into equation (1), the time-domain function of the frequency response is obtained through the inverse Laplace transform:

[0089]

[0090] In the formula: ε represents the unit step function

[0091] 1.2 Frequency Response Stage Analysis

[0092] Taking 50Hz as the frequency under normal operating conditions, based on the above frequency response formula, the system frequency formula under short-time disturbances is obtained as follows:

[0093]

[0094] 1) 0 ≤ t < t1 + t y stage

[0095] During this phase, the system frequency continues to drop, but the rate of drop gradually slows down. Simultaneously, to ensure that the energy storage releases energy before the system frequency reaches its lowest point, an emergency control start-up delay time t is implemented. y The following constraints must be met:

[0096]

[0097] 2)t1+t y ≤t<t1+t y +t3 stage

[0098] During this stage, the energy storage begins to release energy, helping the system return to normal operating conditions. The impact of energy storage on the system at this point can be divided into two scenarios: if... In this scenario, distributed energy storage can immediately restore the frequency from a decrease to an increase. There exists a minimum point, and the formulas for the time and frequency of this point are as follows:

[0099]

[0100] In the formula: t b1 The time for the first minimum point; f b1 This represents the frequency of the first local minimum.

[0101] Another one is In this situation, the total output of distributed energy storage is relatively small and cannot immediately change the rate of change of the system frequency. We will discuss two categories: the first is where the energy storage output is too small, and the frequency continues to decrease even after emergency control measures are implemented. s And t3 satisfy the relation The second type is energy storage, which can enable the system to recover and rise during emergency control periods, P s The relationship between t3 and t3 is the opposite of that in the first type. This type of case also has a local point, and the formulas for the time and frequency of this point are as follows:

[0102]

[0103] 3)t1+t y +t3≤t<t1+t2 stage

[0104] During this phase, distributed energy storage exits emergency control, but the power deficit causes the system frequency to drop again. This can be discussed in two scenarios:

[0105] The first scenario involves excessive energy release from distributed energy storage, causing the system to overshoot in the second stage. In this stage, the overshoot decreases and approaches normal operating conditions, satisfying the relevant parameters. There is no second minimum point in this case.

[0106] The second scenario is that the energy released by distributed energy storage is insufficient to cause system overshoot, but the frequency recovers slowly under the influence of system inertia and damping. In this case, there is a second minimum point, which must satisfy the opposite relationship to the first scenario. The formulas for the time and frequency of the minimum point are as follows:

[0107]

[0108] In the formula: t b2 The time for the second minimum point; f b2 This represents the frequency of the second minimum point.

[0109] 1.3 Energy Release Optimization Model

[0110] Using the minimum energy released from energy storage M as the objective function for the control strategy, the optimization model is as follows:

[0111]

[0112] In the formula: f limit This is the lowest system frequency; f b The frequency of each local minimum point; P represents the rate of change of the system's frequency before the distributed energy storage system ceases to output power; sN It is the sum of the rated power of distributed energy storage.

[0113] 1.4 Improved FOX Algorithm Model

[0114] The FOX algorithm is a heuristic algorithm developed by mimicking the fox's hunting process. It updates the population position based on the principle of measuring the distance between the fox and its prey to execute efficient jumping techniques. Although the FOX algorithm has excellent population exploration capabilities, its ability to develop optimal solutions is insufficient. To address this issue, this patent combines grid pattern search technology with the FOX algorithm to enhance its development capabilities. The improved FOX algorithm consists of three stages, as follows: Figure 3 As shown.

[0115] The first phase is the development phase, which is divided into two development strategies, and the calculation formula is shown below:

[0116]

[0117] In the formula: Jump it Dis_FP is the jump height. it Dis_ST is the prey distance. it Sp_S represents the distance sound travels; Time_ST represents the speed of sound propagation; Sp_S represents the distance sound travels. it A random number between 0 and 1; BestX it The optimal population position is represented by 'it', where 'it' is the current iteration number.

[0118] Based on the random variable P, two strategies will be used for position updates, as shown in the following formulas:

[0119]

[0120] In the formula: X (it+1) The updated individual variables; c1 and c2 are 0.18 and 0.82 respectively; p is a random number between 0 and 1.

[0121] The second stage is the exploration stage, and the formula is as follows:

[0122]

[0123] In the formula: d represents the population dimension; MinT is the minimum time variable; a is the control variable; max it tt represents the maximum number of iterations; tt represents the propagation time.

[0124] The third stage employs a grid search, establishing a search grid centered on the current optimal location with a set step size. The grid establishment method is as follows:

[0125] x i =x0+v(j)·L s (13)

[0126] In the formula: x0 and x iThese refer to the starting point and grid point, respectively; v(j) refers to the mode vector; L s Refers to the search step size.

[0127] After establishing the search grid, select the points with better fitness from the grid points as the starting point for the next iteration. After multiple iterations, explore a better solution.

[0128] This patent will solve the energy release optimization model of equation (9) based on the above algorithm model, with P s A random population is constructed using t3 as the individual variable, releasing energy P. s *t3 is the fitness calculation formula. It iteratively updates the variables of individuals in the population and selects the individual with the smallest fitness as the optimal solution.

[0129] Combination Figure 2 As shown, this scheme can be verified and calculated based on an improved IEEE 10-machine 39-node simulation system. The system short-circuit fault occurs between nodes 2 and 3, with a grounding impedance of 0, and is cleared 0.1 seconds after the fault occurs. The system structure is as follows. Figure 2 As shown, nodes 30, 31, 35, 36, 37, 38, and 39 are synchronous machines with an inertial time constant Hg = 4s; nodes 32, 33, and 34 are doubly-fed wind turbines, each with a rated power of 1.5MW, and the number of turbines is 100, 50, and 50 respectively, with outputs of 100MW, 50MW, and 50MW; nodes 9, 12, 24, and 27 are distributed energy storage stations, each with a rated power of 200MW, 100MW, 50MW, and 50MW respectively. The total load is 1000MW, using a constant power model, and the active frequency response coefficient of the load is L = 2.

[0130] Based on the above data, the distributed energy storage emergency control energy release method proposed in this patent is used to solve the optimization model. The total energy released by the distributed energy storage is allocated according to the capacity of each distributed energy storage. The energy release strategy of each distributed energy storage is shown in Table 1.

[0131] Table 1 System Output Table

[0132]

[0133] Based on the energy release strategy calculated in Table 1, the frequency response results are shown in the frequency curve. Figure 4 As shown, comparison Figure 4The frequency impact curves of the power system obtained by different energy release strategies show that the energy calculation method proposed in this patent can reasonably schedule distributed energy storage to participate in emergency control, and can control the lowest point of the system frequency at 49.5Hz. Compared with the lowest point of 49.36Hz under the control method, it more effectively handles the problem of transient frequency exceeding the limit under short-term power surges. In addition, it can be seen that the energy release strategy obtained by the method of this patent can help the system recover to the normal power frequency more quickly.

[0134] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for assessing the minimum energy release for emergency control of distributed energy storage based on an improved FOX algorithm, characterized in that, It includes: A frequency response model is constructed and segmented. The frequency response process of the distributed energy storage system is analyzed according to different time periods to determine and calculate the frequency minimum value and its magnitude in each time period. Based on this, an energy release optimization model for emergency control of distributed energy storage is constructed. By combining grid pattern search with the FOX algorithm, an improved FOX algorithm model is constructed and used for parameter optimization of the energy release optimization model. Then, the minimum release energy of the distributed energy storage system under short-circuit fault is evaluated through the energy release optimization model. Among them, combining grid pattern search with the FOX algorithm to construct an improved FOX algorithm includes: 1.4 Constructing an improved FOX algorithm model The improved FOX algorithm consists of three stages. The first stage is the development stage, which is divided into two development strategies. The calculation formula is shown below: In the formula: Jump it Dis_FP is the jump height. it Dis_ST is the prey distance. it Sp_S represents the distance sound travels; Time_ST represents the speed of sound propagation; Sp_S represents the distance sound travels. it A random number between 0 and 1; BestX it The optimal population position is represented by `it`, which is the current iteration number. Based on the random variable P, two strategies will be used for position updates, as shown in the following formulas: In the formula: X (it+1) The updated individual variables; c1 and c2 are 0.18 and 0.82 respectively; p is a random number between 0 and 1; The second stage is the exploration stage, and the formula is as follows: In the formula: d represents the population dimension; MinT is the minimum time variable; a is the control variable; max it tt represents the maximum number of iterations; tt represents the propagation time. The third stage employs a grid search, establishing a search grid centered on the current optimal location with a set step size. The grid establishment method is as follows: x i =x0+v(j)·L s In the formula: x0 and x i These refer to the starting point and grid point, respectively; v(j) refers to the mode vector; L s Refers to the search step size; After establishing the search grid, select the points with better fitness from the grid points as the starting point for the next iteration. After multiple iterations, explore a better solution.

2. The method for assessing the minimum energy release for emergency control of distributed energy storage based on the improved FOX algorithm as described in claim 1, characterized in that, It also includes: The minimum energy release for emergency control of a distributed energy storage system under short-circuit fault is assessed, and then the output task is allocated based on the capacity of each distributed energy storage unit within the system.

3. The method for assessing the minimum energy release for emergency control of distributed energy storage based on the improved FOX algorithm as described in claim 1 or 2, characterized in that, A frequency response model is constructed and segmented. The frequency response process of the distributed energy storage system is analyzed according to different time periods to determine and calculate the frequency minimum values ​​and their magnitudes for each time period, including: 1.1 Constructing a frequency response model for distributed energy storage to participate in emergency control The total energy released by each distributed energy storage unit in the distributed energy storage system, P s As a disturbance, the mathematical model of the frequency response of a distributed energy storage system is defined as follows: Wherein: Δω represents the system frequency response function; ΔG represents the frequency-active power transfer function of the distributed energy storage system; ΔP is the power disturbance; M and L refer to the rotor time constant and active frequency response coefficient related to the moment of inertia, respectively; The distributed energy storage system is used for energy storage in new energy power generation. By ignoring the influence of load during transient processes, the non-step power disturbance is composed only of the short-term impact power during the low-voltage period of the new energy source and the energy released by the energy storage. Its frequency domain expression is defined as follows: Wherein: P is the short-term impact power generated during the low-voltage period of the new energy source; P s t1 represents the total energy released by distributed energy storage; t2 represents the low-voltage recovery time of the new energy source; t3 represents the energy release duration of the energy storage; t y To control the start-up delay time in emergencies; Substituting equation (2) into equation (1), the time-domain function of the frequency response is obtained through the inverse Laplace transform: Wherein, ε represents the unit step function; 1.2 Frequency Response Stage Analysis Taking 50Hz as the frequency under normal operating conditions, based on the model formula of the frequency response model, the formula for the frequency of the distributed energy storage system under short-term disturbances is defined as follows: Here, the time interval containing t corresponds to different segments of the model.

4. The method for assessing the minimum energy release for emergency control of distributed energy storage based on the improved FOX algorithm as described in claim 3, characterized in that, Frequency response phase analysis also includes: 1) 0≤t <t1+t y stage During this phase, the frequency of the distributed energy storage system continues to drop, but the rate of drop gradually slows down. To ensure that the energy storage system releases energy before the system frequency reaches its lowest point, an emergency control start-up delay time t is implemented. y The following constraints must be met: 2)t1+t y ≤t <t1+t y +t3 stage During this stage, energy storage begins to release energy, helping the distributed energy storage system return to normal operation. At this point, the impact of energy storage on the distributed energy storage system can be divided into two scenarios: like In this scenario, distributed energy storage can immediately restore the frequency from a decrease to an increase. There exists a minimum point, and the formulas for the time and frequency of this point are as follows: In the formula: t b1 The time for the first minimum point; f b1 This represents the frequency of the first local minimum point; like In this case, the total output of distributed energy storage is relatively small and cannot immediately change the rate of change of the system frequency. Therefore, it is discussed in two categories: The first type is where the energy storage output is too small, and the frequency continues to drop even after emergency control measures are implemented. s And t3 satisfy the following relationship: The second type is energy storage, which can enable the system to recover and rise during emergency control periods, P s The relationship between t3 and t3 is the opposite of that in the first type. This type of case also has a local point, and the formulas for the time and frequency of this point are as follows: 3) t1 + t y + t3 ≤ t < t1 + t2 phase During this phase, distributed energy storage exits emergency control, but the power deficit causes the system frequency to drop again. This can be discussed in two scenarios: The first scenario involves excessive energy release from distributed energy storage, causing the system to overshoot in the second stage. In this stage, the overshoot decreases and approaches normal operating conditions, requiring the following relationship to be satisfied: There is no second minimum point in this case; The second scenario is that the energy released by the distributed energy storage is insufficient to cause overshoot in the distributed energy storage system, but the frequency recovers slowly under the influence of system inertia and damping. In this case, there is a second minimum point, which must satisfy the opposite relationship to the first scenario. The time and frequency of the minimum point are determined by the following formulas: In the formula: t b2 The time for the second minimum point; f b2 This represents the frequency of the second minimum point.

5. The method for assessing the minimum energy release for emergency control of distributed energy storage based on the improved FOX algorithm as described in claim 4, characterized in that, The energy release optimization model for emergency control of distributed energy storage includes: 1.3 Constructing an energy release optimization model The optimization model is defined as follows, with the minimum energy released from energy storage M as the objective function for the control strategy: Wherein: f limit This is the lowest system frequency; f b The frequency of each local minimum point; P represents the rate of change of the system's frequency before the distributed energy storage system ceases to output power; sN It is the sum of the rated power of distributed energy storage.

6. The method for assessing the minimum energy release for emergency control of distributed energy storage based on the improved FOX algorithm as described in claim 5, characterized in that, The improved FOX algorithm model is used for parameter optimization of the energy release optimization model in equation (9), with P s A random population is constructed using t3 as the individual variable, releasing energy P. s *t3 is the fitness calculation formula. It iteratively updates the variables of individuals in the population and selects the individual with the smallest fitness as the optimal solution.

7. The method for assessing the minimum energy release for emergency control of distributed energy storage based on the improved FOX algorithm as described in claim 6, characterized in that, The improved FOX algorithm model is based on the IEEE 10-machine 39-node system.

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